Thermal and statistical physics h. gould, j. tobochnik-1

Contents
1 From Microscopic to Macroscopic Behavior 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Some qualitative observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Doing work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Quality of energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.5 Some simple simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.6 Work, heating, and the ﬁrst law of thermodynamics . . . . . . . . . . . . . . . . . 14
1.7 Measuring the pressure and temperature . . . . . . . . . . . . . . . . . . . . . . . . 15
1.8 *The fundamental need for a statistical approach . . . . . . . . . . . . . . . . . . . 18
1.9 *Time and ensemble averages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.10 *Models of matter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.10.1 The ideal gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.10.2 Interparticle potentials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.10.3 Lattice models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.11 Importance of simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.12 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Additional Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Suggestions for Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2 Thermodynamic Concepts 26
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.2 The system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.3 Thermodynamic Equilibrium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.4 Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.5 Pressure Equation of State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.6 Some Thermodynamic Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.7 Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
i

CONTENTS ii
2.8 The First Law of Thermodynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.9 Energy Equation of State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.10 Heat Capacities and Enthalpy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.11 Adiabatic Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.12 The Second Law of Thermodynamics . . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.13 The Thermodynamic Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
2.14 The Second Law and Heat Engines . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
2.15 Entropy Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
2.16 Equivalence of Thermodynamic and Ideal Gas Scale Temperatures . . . . . . . . . 60
2.17 The Thermodynamic Pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
2.18 The Fundamental Thermodynamic Relation . . . . . . . . . . . . . . . . . . . . . . 62
2.19 The Entropy of an Ideal Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
2.20 The Third Law of Thermodynamics . . . . . . . . . . . . . . . . . . . . . . . . . . 64
2.21 Free Energies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Appendix 2B: Mathematics of Thermodynamics . . . . . . . . . . . . . . . . . . . . . . 70
3 Concepts of Probability 82
3.1 Probability in everyday life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
3.2 The rules of probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
3.3 Mean values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
3.4 The meaning of probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
3.4.1 Information and uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
3.4.2 *Bayesian inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
3.5 Bernoulli processes and the binomial distribution . . . . . . . . . . . . . . . . . . . 99
3.6 Continuous probability distributions . . . . . . . . . . . . . . . . . . . . . . . . . . 109
3.7 The Gaussian distribution as a limit of the binomial distribution . . . . . . . . . . 111
3.8 The central limit theorem or why is thermodynamics possible? . . . . . . . . . . . 113
3.9 The Poisson distribution and should you fly in airplanes? . . . . . . . . . . . . . . 116
3.10 *Traffic flow and the exponential distribution . . . . . . . . . . . . . . . . . . . . . 117
3.11 *Are all probability distributions Gaussian? . . . . . . . . . . . . . . . . . . . . . . 119

CONTENTS iii
4 Statistical Mechanics 138
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
4.2 A simple example of a thermal interaction . . . . . . . . . . . . . . . . . . . . . . . 140
4.3 Counting microstates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
4.3.1 Noninteracting spins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
4.3.2 *One-dimensional Ising model . . . . . . . . . . . . . . . . . . . . . . . . . . 150
4.3.3 A particle in a one-dimensional box . . . . . . . . . . . . . . . . . . . . . . 151
4.3.4 One-dimensional harmonic oscillator . . . . . . . . . . . . . . . . . . . . . . 153
4.3.5 One particle in a two-dimensional box . . . . . . . . . . . . . . . . . . . . . 154
4.3.6 One particle in a three-dimensional box . . . . . . . . . . . . . . . . . . . . 156
4.3.7 Two noninteracting identical particles and the semiclassical limit . . . . . . 156
4.4 The number of states of N noninteracting particles: Semiclassical limit . . . . . . . 158
4.5 The microcanonical ensemble (fixed E, V, and N) . . . . . . . . . . . . . . . . . . . 160
4.6 Systems in contact with a heat bath: The canonical ensemble (fixed T, V, and N) 165
4.7 Connection between statistical mechanics and thermodynamics . . . . . . . . . . . 170
4.8 Simple applications of the canonical ensemble . . . . . . . . . . . . . . . . . . . . . 172
4.9 A simple thermometer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
4.10 Simulations of the microcanonical ensemble . . . . . . . . . . . . . . . . . . . . . . 177
4.11 Simulations of the canonical ensemble . . . . . . . . . . . . . . . . . . . . . . . . . 178
4.12 Grand canonical ensemble (fixed T, V, and µ) . . . . . . . . . . . . . . . . . . . . . 179
4.13 Entropy and disorder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
Appendix 4A: The Volume of a Hypersphere . . . . . . . . . . . . . . . . . . . . . . . . 183
Appendix 4B: Fluctuations in the Canonical Ensemble . . . . . . . . . . . . . . . . . . . 184
5 Magnetic Systems 190
5.1 Paramagnetism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
5.2 Thermodynamics of magnetism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
5.3 The Ising model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
5.4 The Ising Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
5.4.1 Exact enumeration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
5.4.2 ∗
Spin-spin correlation function . . . . . . . . . . . . . . . . . . . . . . . . . 199
5.4.3 Simulations of the Ising chain . . . . . . . . . . . . . . . . . . . . . . . . . . 201
5.4.4 *Transfer matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
5.4.5 Absence of a phase transition in one dimension . . . . . . . . . . . . . . . . 205
5.5 The Two-Dimensional Ising Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 206

CONTENTS iv
5.5.1 Onsager solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
5.5.2 Computer simulation of the two-dimensional Ising model . . . . . . . . . . 211
5.6 Mean-Field Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
5.7 *Inﬁnite-range interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
6 Noninteracting Particle Systems 230
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230
6.2 The Classical Ideal Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230
6.3 Classical Systems and the Equipartition Theorem . . . . . . . . . . . . . . . . . . . 238
6.4 Maxwell Velocity Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240
6.5 Occupation Numbers and Bose and Fermi Statistics . . . . . . . . . . . . . . . . . 243
6.6 Distribution Functions of Ideal Bose and Fermi Gases . . . . . . . . . . . . . . . . 245
6.7 Single Particle Density of States . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
6.7.1 Photons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249
6.7.2 Electrons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250
6.8 The Equation of State for a Noninteracting Classical Gas . . . . . . . . . . . . . . 252
6.9 Black Body Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
6.10 Noninteracting Fermi Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259
6.10.1 Ground-state properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260
6.10.2 Low temperature thermodynamic properties . . . . . . . . . . . . . . . . . . 263
6.11 Bose Condensation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267
6.12 The Heat Capacity of a Crystalline Solid . . . . . . . . . . . . . . . . . . . . . . . . 272
6.12.1 The Einstein model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272
6.12.2 Debye theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
Appendix 6A: Low Temperature Expansion . . . . . . . . . . . . . . . . . . . . . . . . . 275
7 Thermodynamic Relations and Processes 288
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288
7.2 Maxwell Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290
7.3 Applications of the Maxwell Relations . . . . . . . . . . . . . . . . . . . . . . . . . 291
7.3.1 Internal energy of an ideal gas . . . . . . . . . . . . . . . . . . . . . . . . . 291
7.3.2 Relation between the speciﬁc heats . . . . . . . . . . . . . . . . . . . . . . . 291
7.4 Applications to Irreversible Processes . . . . . . . . . . . . . . . . . . . . . . . . . . 292
7.4.1 The Joule or free expansion process . . . . . . . . . . . . . . . . . . . . . . 293

CONTENTS v
7.4.2 Joule-Thomson process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294
7.5 Equilibrium Between Phases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296
7.5.1 Equilibrium conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297
7.5.2 Clausius-Clapeyron equation . . . . . . . . . . . . . . . . . . . . . . . . . . 298
7.5.3 Simple phase diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300
7.5.4 Pressure dependence of the melting point . . . . . . . . . . . . . . . . . . . 301
7.5.5 Pressure dependence of the boiling point . . . . . . . . . . . . . . . . . . . . 302
7.5.6 The vapor pressure curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302
7.6 Vocabulary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303
8 Classical Gases and Liquids 306
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306
8.2 The Free Energy of an Interacting System . . . . . . . . . . . . . . . . . . . . . . . 306
8.3 Second Virial Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309
8.4 Cumulant Expansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313
8.5 High Temperature Expansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315
8.6 Density Expansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319
8.7 Radial Distribution Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
8.7.1 Relation of thermodynamic functions to g(r) . . . . . . . . . . . . . . . . . 326
8.7.2 Relation of g(r) to static structure function S(k) . . . . . . . . . . . . . . . 327
8.7.3 Variable number of particles . . . . . . . . . . . . . . . . . . . . . . . . . . . 329
8.7.4 Density expansion of g(r) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331
8.8 Computer Simulation of Liquids . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331
8.9 Perturbation Theory of Liquids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333
8.9.1 The van der Waals Equation . . . . . . . . . . . . . . . . . . . . . . . . . . 334
8.9.2 Chandler-Weeks-Andersen theory . . . . . . . . . . . . . . . . . . . . . . . . 335
8.10 *The Ornstein-Zernicke Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336
8.11 *Integral Equations for g(r) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337
8.12 *Coulomb Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339
8.12.1 Debye-Hückel Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340
8.12.2 Linearized Debye-Hückel approximation . . . . . . . . . . . . . . . . . . . . 341
8.12.3 Diagrammatic Expansion for Charged Particles . . . . . . . . . . . . . . . . 342
8.13 Vocabulary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343
Appendix 8A: The third virial coefficient for hard spheres . . . . . . . . . . . . . . . . . 344
8.14 Additional Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347

CONTENTS vi
9 Critical Phenomena 350
9.1 A Geometrical Phase Transition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350
9.2 Renormalization Group for Percolation . . . . . . . . . . . . . . . . . . . . . . . . . 354
9.3 The Liquid-Gas Transition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358
9.4 Bethe Approximation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361
9.5 Landau Theory of Phase Transitions . . . . . . . . . . . . . . . . . . . . . . . . . . 363
9.6 Other Models of Magnetism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369
9.7 Universality and Scaling Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . 371
9.8 The Renormalization Group and the 1D Ising Model . . . . . . . . . . . . . . . . . 372
9.9 The Renormalization Group and the Two-Dimensional Ising Model . . . . . . . . . 376
9.10 Vocabulary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382
9.11 Additional Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382
10 Introduction to Many-Body Perturbation Theory 387
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387
10.2 Occupation Number Representation . . . . . . . . . . . . . . . . . . . . . . . . . . 388
10.3 Operators in the Second Quantization Formalism . . . . . . . . . . . . . . . . . . . 389
10.4 Weakly Interacting Bose Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390
A Useful Formulae 397
A.1 Physical constants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397
A.2 SI derived units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397
A.3 Conversion factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398
A.4 Mathematical Formulae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398
A.5 Approximations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398
A.6 Euler-Maclaurin formula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399
A.7 Gaussian Integrals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399
A.8 Stirling’s formula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400
A.9 Constants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401
A.10 Probability distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402
A.11 Fermi integrals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402
A.12 Bose integrals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403

Chapter 1
From Microscopic to Macroscopic
Behavior
c 2006 by Harvey Gould and Jan Tobochnik
28 August 2006
The goal of this introductory chapter is to explore the fundamental differences between micro-
scopic and macroscopic systems and the connections between classical mechanics and statistical
mechanics. We note that bouncing balls come to rest and hot objects cool, and discuss how the
behavior of macroscopic objects is related to the behavior of their microscopic constituents. Com-
puter simulations will be introduced to demonstrate the relation of microscopic and macroscopic
behavior.
1.1 Introduction
Our goal is to understand the properties of macroscopic systems, that is, systems of many elec-
trons, atoms, molecules, photons, or other constituents. Examples of familiar macroscopic objects
include systems such as the air in your room, a glass of water, a copper coin, and a rubber band
(examples of a gas, liquid, solid, and polymer, respectively). Less familiar macroscopic systems
are superconductors, cell membranes, the brain, and the galaxies.
We will find that the type of questions we ask about macroscopic systems differ in important
ways from the questions we ask about microscopic systems. An example of a question about a
microscopic system is “What is the shape of the trajectory of the Earth in the solar system?”
In contrast, have you ever wondered about the trajectory of a particular molecule in the air of
your room? Why not? Is it relevant that these molecules are not visible to the eye? Examples of
questions that we might ask about macroscopic systems include the following:
1. How does the pressure of a gas depend on the temperature and the volume of its container?
2. How does a refrigerator work? What is its maximum efficiency?
1

CHAPTER 1. FROM MICROSCOPIC TO MACROSCOPIC BEHAVIOR 2
3. How much energy do we need to add to a kettle of water to change it to steam?
4. Why are the properties of water different from those of steam, even though water and steam
consist of the same type of molecules?
5. How are the molecules arranged in a liquid?
6. How and why does water freeze into a particular crystalline structure?
7. Why does iron lose its magnetism above a certain temperature?
8. Why does helium condense into a superfluid phase at very low temperatures? Why do some
materials exhibit zero resistance to electrical current at sufficiently low temperatures?
9. How fast does a river current have to be before its flow changes from laminar to turbulent?
10. What will the weather be tomorrow?
The above questions can be roughly classified into three groups. Questions 1–3 are concerned
with macroscopic properties such as pressure, volume, and temperature and questions related to
heating and work. These questions are relevant to thermodynamics which provides a framework
for relating the macroscopic properties of a system to one another. Thermodynamics is concerned
only with macroscopic quantities and ignores the microscopic variables that characterize individual
molecules. For example, we will find that understanding the maximum efficiency of a refrigerator
does not require a knowledge of the particular liquid used as the coolant. Many of the applications
of thermodynamics are to thermal engines, for example, the internal combustion engine and the
steam turbine.
Questions 4–8 relate to understanding the behavior of macroscopic systems starting from the
atomic nature of matter. For example, we know that water consists of molecules of hydrogen
and oxygen. We also know that the laws of classical and quantum mechanics determine the
behavior of molecules at the microscopic level. The goal of statistical mechanics is to begin with
the microscopic laws of physics that govern the behavior of the constituents of the system and
deduce the properties of the system as a whole. Statistical mechanics is the bridge between the
microscopic and macroscopic worlds.
Thermodynamics and statistical mechanics assume that the macroscopic properties of the
system do not change with time on the average. Thermodynamics describes the change of a
macroscopic system from one equilibrium state to another. Questions 9 and 10 concern macro-
scopic phenomena that change with time. Related areas are nonequilibrium thermodynamics and
fluid mechanics from the macroscopic point of view and nonequilibrium statistical mechanics from
the microscopic point of view. Although there has been progress in our understanding of nonequi-
librium phenomena such as turbulent flow and hurricanes, our understanding of nonequilibrium
phenomena is much less advanced than our understanding of equilibrium systems. Because un-
derstanding the properties of macroscopic systems that are independent of time is easier, we will
focus our attention on equilibrium systems and consider questions such as those in Questions 1–8.

1.2 Some qualitative observations
We begin our discussion of macroscopic systems by considering a glass of water. We know that if
we place a glass of hot water into a cool room, the hot water cools until its temperature equals
that of the room. This simple observation illustrates two important properties associated with
macroscopic systems – the importance of temperature and the arrow of time. Temperature is
familiar because it is associated with the physiological sensation of hot and cold and is important
in our everyday experience. We will find that temperature is a subtle concept.
The direction or arrow of time is an even more subtle concept. Have you ever observed a glass
of water at room temperature spontaneously become hotter? Why not? What other phenomena
exhibit a direction of time? Time has a direction as is expressed by the nursery rhyme:
Humpty Dumpty sat on a wall
Humpty Dumpty had a great fall
All the king’s horses and all the king’s men
Couldn’t put Humpty Dumpty back together again.
Is there a a direction of time for a single particle? Newton’s second law for a single particle,
F = dp/dt, implies that the motion of particles is time reversal invariant, that is, Newton’s second
law looks the same if the time t is replaced by −t and the momentum p by −p. There is no
direction of time at the microscopic level. Yet if we drop a basketball onto a floor, we know that it
will bounce and eventually come to rest. Nobody has observed a ball at rest spontaneously begin
to bounce, and then bounce higher and higher. So based on simple everyday observations, we can
conclude that the behavior of macroscopic bodies and single particles is very different.
Unlike generations of about a century or so ago, we know that macroscopic systems such as a
glass of water and a basketball consist of many molecules. Although the intermolecular forces in
water produce a complicated trajectory for each molecule, the observable properties of water are
easy to describe. Moreover, if we prepare two glasses of water under similar conditions, we would
find that the observable properties of the water in each glass are indistinguishable, even though
the motion of the individual particles in the two glasses would be very different.
Because the macroscopic behavior of water must be related in some way to the trajectories of its
constituent molecules, we conclude that there must be a relation between the notion of temperature
and mechanics. For this reason, as we discuss the behavior of the macroscopic properties of a glass
of water and a basketball, it will be useful to discuss the relation of these properties to the motion
of their constituent molecules.
For example, if we take into account that the bouncing ball and the floor consist of molecules,
then we know that the total energy of the ball and the floor is conserved as the ball bounces
and eventually comes to rest. What is the cause of the ball eventually coming to rest? You
might be tempted to say the cause is “friction,” but friction is just a name for an effective or
phenomenological force. At the microscopic level we know that the fundamental forces associated
with mass, charge, and the nucleus conserve the total energy. So if we take into account the
molecules of the ball and the floor, their total energy is conserved. Conservation of energy does
not explain why the inverse process does not occur, because such a process also would conserve
the total energy. So a more fundamental explanation is that the ball comes to rest consistent with
conservation of the total energy and consistent with some other principle of physics. We will learn

that this principle is associated with an increase in the entropy of the system. For now, entropy is
only a name, and it is important only to understand that energy conservation is not sufficient to
understand the behavior of macroscopic systems. (As for most concepts in physics, the meaning
of entropy in the context of thermodynamics and statistical mechanics is very different than the
way entropy is used by nonscientists.)
For now, the nature of entropy is vague, because we do not have an entropy meter like we do
for energy and temperature. What is important at this stage is to understand why the concept of
energy is not sufficient to describe the behavior of macroscopic systems.
By thinking about the constituent molecules, we can gain some insight into the nature of
entropy. Let us consider the ball bouncing on the floor again. Initially, the energy of the ball
is associated with the motion of its center of mass, that is, the energy is associated with one
degree of freedom. However, after some time, the energy becomes associated with many degrees
of freedom associated with the individual molecules of the ball and the floor. If we were to bounce
the ball on the floor many times, the ball and the floor would each feel warm to our hands. So we
can hypothesize that energy has been transferred from one degree of freedom to many degrees of
freedom at the same time that the total energy has been conserved. Hence, we conclude that the
entropy is a measure of how the energy is distributed over the degrees of freedom.
What other quantities are associated with macroscopic systems besides temperature, energy,
and entropy? We are already familiar with some of these quantities. For example, we can measure
the air pressure in a basketball and its volume. More complicated quantities are the thermal
conductivity of a solid and the viscosity of oil. How are these macroscopic quantities related to
each other and to the motion of the individual constituent molecules? The answers to questions
such as these and the meaning of temperature and entropy will take us through many chapters.
1.3 Doing work
We already have observed that hot objects cool, and cool objects do not spontaneously become
hot; bouncing balls come to rest, and a stationary ball does not spontaneously begin to bounce.
And although the total energy must be conserved in any process, the distribution of energy changes
in an irreversible manner. We also have concluded that a new concept, the entropy, needs to be
introduced to explain the direction of change of the distribution of energy.
Now let us take a purely macroscopic viewpoint and discuss how we can arrive at a similar
qualitative conclusion about the asymmetry of nature. This viewpoint was especially important
historically because of the lack of a microscopic theory of matter in the 19th century when the
laws of thermodynamics were being developed.
Consider the conversion of stored energy into heating a house or a glass of water. The stored
energy could be in the form of wood, coal, or animal and vegetable oils for example. We know that
this conversion is easy to do using simple methods, for example, an open fireplace. We also know
that if we rub our hands together, they will become warmer. In fact, there is no theoretical limit1
to the efficiency at which we can convert stored energy to energy used for heating an object.
What about the process of converting stored energy into work? Work like many of the other
concepts that we have mentioned is difficult to define. For now let us say that doing work is
1Of course, the efficiency cannot exceed 100%.

equivalent to the raising of a weight (see Problem 1.18). To be useful, we need to do this conversion
in a controlled manner and indefinitely. A single conversion of stored energy into work such as the
explosion of a bomb might do useful work, such as demolishing an unwanted football stadium, but
a bomb is not a useful device that can be recycled and used again. It is much more difficult to
convert stored energy into work and the discovery of ways to do this conversion led to the industrial
revolution. In contrast to the primitiveness of the open hearth, we have to build an engine to do
this conversion.
Can we convert stored energy into work with 100% efficiency? On the basis of macroscopic
arguments alone, we cannot answer this question and have to appeal to observations. We know
that some forms of stored energy are more useful than others. For example, why do we bother to
burn coal and oil in power plants even though the atmosphere and the oceans are vast reservoirs
of energy? Can we mitigate global warming by extracting energy from the atmosphere to run a
power plant? From the work of Kelvin, Clausius, Carnot and others, we know that we cannot
convert stored energy into work with 100% efficiency, and we must necessarily “waste” some of
the energy. At this point, it is easier to understand the reason for this necessary inefficiency by
microscopic arguments. For example, the energy in the gasoline of the fuel tank of an automobile
is associated with many molecules. The job of the automobile engine is to transform this energy
so that it is associated with only a few degrees of freedom, that is, the rolling tires and gears. It
is plausible that it is inefficient to transfer energy from many degrees of freedom to only a few.
In contrast, transferring energy from a few degrees of freedom (the firewood) to many degrees of
freedom (the air in your room) is relatively easy.
The importance of entropy, the direction of time, and the inefficiency of converting stored
energy into work are summarized in the various statements of the second law of thermodynamics.
It is interesting that historically, the second law of thermodynamics was conceived before the first
law. As we will learn in Chapter 2, the first law is a statement of conservation of energy.
1.4 Quality of energy
Because the total energy is conserved (if all energy transfers are taken into account), why do we
speak of an “energy shortage”? The reason is that energy comes in many forms and some forms are
more useful than others. In the context of thermodynamics, the usefulness of energy is determined
by its ability to do work.
Suppose that we take some firewood and use it to “heat” a sealed room. Because of energy
conservation, the energy in the room plus the firewood is the same before and after the firewood
has been converted to ash. But which form of the energy is more capable of doing work? You
probably realize that the firewood is a more useful form of energy than the “hot air” that exists
after the firewood is burned. Originally the energy was stored in the form of chemical (potential)
energy. Afterward the energy is mostly associated with the motion of the molecules in the air.
What has changed is not the total energy, but its ability to do work. We will learn that an increase
in entropy is associated with a loss of ability to do work. We have an entropy problem, not an
energy shortage.

1.5 Some simple simulations
So far we have discussed the behavior of macroscopic systems by appealing to everyday experience
and simple observations. We now discuss some simple ways that we can simulate the behavior of
macroscopic systems, which consist of the order of 1023
particles. Although we cannot simulate
such a large system on a computer, we will find that even relatively small systems of the order of
a hundred particles are sufficient to illustrate the qualitative behavior of macroscopic systems.
Consider a macroscopic system consisting of particles whose internal structure can be ignored.
In particular, imagine a system of N particles in a closed container of volume V and suppose that
the container is far from the influence of external forces such as gravity. We will usually consider
two-dimensional systems so that we can easily visualize the motion of the particles.
For simplicity, we assume that the motion of the particles is given by classical mechanics,
that is, by Newton’s second law. If the resultant equations of motion are combined with initial
conditions for the positions and velocities of each particle, we can calculate, in principle, the
trajectory of each particle and the evolution of the system. To compute the total force on each
particle we have to specify the nature of the interaction between the particles. We will assume
that the force between any pair of particles depends only on the distance between them. This
simplifying assumption is applicable to simple liquids such as liquid argon, but not to water. We
will also assume that the particles are not charged. The force between any two particles must be
repulsive when their separation is small and weakly attractive when they are reasonably far apart.
For simplicity, we will usually assume that the interaction is given by the Lennard-Jones potential,
whose form is given by
u(r) = 4
σ
r
12
−
σ
r
6
. (1.1)
A plot of the Lennard-Jones potential is shown in Figure 1.1. The r−12
form of the repulsive part
of the interaction is chosen for convenience only and has no fundamental significance. However,
the attractive 1/r6
behavior at large r is the van der Waals interaction. The force between any
two particles is given by f(r) = −du/dr.
Usually we want to simulate a gas or liquid in the bulk. In such systems the fraction of
particles near the walls of the container is negligibly small. However, the number of particles that
can be studied in a simulation is typically 103
–106
. For these relatively small systems, the fraction
of particles near the walls of the container would be significant, and hence the behavior of such
a system would be dominated by surface effects. The most common way of minimizing surface
effects and to simulate more closely the properties of a bulk system is to use what are known as
toroidal boundary conditions. These boundary conditions are familiar to computer game players.
For example, a particle that exits the right edge of the “box,” re-enters the box from the left side.
In one dimension, this boundary condition is equivalent to taking a piece of wire and making it
into a loop. In this way a particle moving on the wire never reaches the end.
Given the form of the interparticle potential, we can determine the total force on each particle
due to all the other particles in the system. Given this force, we find the acceleration of each
particle from Newton’s second law of motion. Because the acceleration is the second derivative
of the position, we need to solve a second-order differential equation for each particle (for each
direction). (For a two-dimensional system of N particles, we would have to solve 2N differential
equations.) These differential equations are coupled because the acceleration of a given particle

u
r
ε
σ
Figure 1.1: Plot of the Lennard-Jones potential u(r), where r is the distance between the particles.
Note that the potential is characterized by a length σ and an energy .
depends on the positions of all the other particles. Obviously, we cannot solve the resultant
set of coupled differential equations analytically. However, we can use relatively straightforward
numerical methods to solve these equations to a good approximation. This way of simulating dense
gases, liquids, solids, and biomolecules is called molecular dynamics.2
Approach to equilibrium. In the following we will explore some of the qualitative properties
of macroscopic systems by doing some simple simulations. Before you actually do the simulations,
think about what you believe the results will be. In many cases, the most valuable part of the sim-
ulation is not the simulation itself, but the act of thinking about a concrete model and its behavior.
The simulations can be run as applications on your computer by downloading the Launcher from
<stp.clarku.edu/simulations/choose.html>. The Launcher conveniently packages all the sim-
ulations (and a few more) discussed in these notes into a single file. Alternatively, you can run
each simulation as an applet using a browser.
Problem 1.1. Approach to equilibrium
Suppose that a box is divided into three equal parts and N particles are placed at random in
the middle third of the box.3
The velocity of each particle is assigned at random and then the
velocity of the center of mass is set to zero. At t = 0, we remove the “barriers” between the
2The nature of molecular dynamics is discussed in Chapter 8 of Gould, Tobochnik, and Christian.
3We have divided the box into three parts so that the effects of the toroidal boundary conditions will not be as
apparent as if we had initially confined the particles to one half of the box. The particles are placed at random in
the middle third of the box with the constraint that no two particles can be closer than the length σ. This constraint
prevents the initial force between any two particles from being two big, which would lead to the breakdown of the
numerical method used to solve the differential equations. The initial density ρ = N/A is ρ = 0.2.

three parts and watch the particles move according to Newton’s equations of motion. We say
that the removal of the barrier corresponds to the removal of an internal constraint. What do
you think will happen? The applet/application at <stp.clarku.edu/simulations/approach.
html> implements this simulation. Give your answers to the following questions before you do the
simulation.
(a) Start the simulation with N = 27, n1 = 0, n2 = N, and n3 = 0. What is the qualitative
behavior of n1, n2, and n3, the number of particles in each third of the box, as a function of
the time t? Does the system appear to show a direction of time? Choose various values of N
that are multiples of three up to N = 270. Is the direction of time better deﬁned for larger N?
(b) Suppose that we made a video of the motion of the particles considered in Problem 1.1a. Would
you be able to tell if the video were played forward or backward for the various values of N?
Would you be willing to make an even bet about the direction of time? Does your conclusion
about the direction of time become more certain as N increases?
(c) After n1, n2, and n3 become approximately equal for N = 270, reverse the time and continue
the simulation. Reversing the time is equivalent to letting t → −t and changing the signs of
all the velocities. Do the particles return to the middle third of the box? Do the simulation
again, but let the particles move for a longer time before the time is reversed. What happens
now?
(d) From watching the motion of the particles, describe the nature of the boundary conditions
that are used in the simulation.
The results of the simulations in Problem 1.1 might not seem very surprising until you start
to think about them. Why does the system as a whole exhibit a direction of time when the motion
of each particle is time reversible? Do the particles ﬁll up the available space simply because the
system becomes less dense?
To gain some more insight into these questions, we consider a simpler simulation. Imagine
a closed box that is divided into two parts of equal volume. The left half initially contains N
identical particles and the right half is empty. We then make a small hole in the partition between
the two halves. What happens? Instead of simulating this system by solving Newton’s equations
for each particle, we adopt a simpler approach based on a probabilistic model. We assume that the
particles do not interact with one another so that the probability per unit time that a particle goes
through the hole in the partition is the same for all particles regardless of the number of particles
in either half. We also assume that the size of the hole is such that only one particle can pass
through it in one unit of time.
One way to implement this model is to choose a particle at random and move it to the other
side. This procedure is cumbersome, because our only interest is the number of particles on each
side. That is, we need to know only n, the number of particles on the left side; the number on
the right side is N − n. Because each particle has the same chance to go through the hole in the
partition, the probability per unit time that a particle moves from left to right equals the number
of particles on the left side divided by the total number of particles; that is, the probability of a
move from left to right is n/N. The algorithm for simulating the evolution of the model is given
by the following steps:

Figure 1.2: Evolution of the number of particles in each third of the box for N = 270. The particles
were initially restricted to the middle third of the box. Toroidal boundary conditions are used in
both directions. The initial velocities were assigned at random from a distribution corresponding
to temperature T = 5. The time was reversed at t ≈ 59. Does the system exhibit a direction of
time?
1. Generate a random number r from a uniformly distributed set of random numbers in the
unit interval 0 ≤ r < 1.
2. If r ≤ n/N, move a particle from left to right, that is, let n → n − 1; otherwise, move a
particle from right to left, n → n + 1.
3. Increase the “time” by 1.
Problem 1.2. Particles in a box
(a) The applet at <stp.clarku.edu/simulations/box.html> implements this algorithm and
plots the evolution of n. Describe the behavior of n(t) for various values of N. Does the
system approach equilibrium? How would you characterize equilibrium? In what sense is
equilibrium better deﬁned as N becomes larger? Does your deﬁnition of equilibrium depend
on how the particles were initially distributed between the two halves of the box?
(b) When the system is in equilibrium, does the number of particles on the left-hand side remain
a constant? If not, how would you describe the nature of equilibrium?
(c) If N 32, does the system ever return to its initial state?

(d) How does n, the mean number of particles on the left-hand side, depend on N after the system
has reached equilibrium? For simplicity, the program computes various averages from time
t = 0. Why would such a calculation not yield the correct equilibrium average values? What
is the purpose of the Zero averages button?
(e) Define the quantity σ by the relation σ2
= (n − n)2. What does σ measure? What would be
its value if n were constant? How does σ depend on N? How does the ratio σ/n depend on
N? In what sense is equilibrium better defined as N increases?
From Problems 1.1 and 1.2 we see that after a system reaches equilibrium, the macroscopic
quantities of interest become independent of time on the average, but exhibit fluctuations about
their average values. We also learned that the relative fluctuations about the average become
smaller as the number of constituents is increased and the details of the dynamics are irrelevant
as far as the general tendency of macroscopic systems to approach equilibrium.
How can we understand why the systems considered in Problems 1.1 and 1.2 exhibit a direction
of time? There are two general approaches that we can take. One way would be to study the
dynamics of the system. A much simpler way is to change the question and take advantage of
the fact that the equilibrium state of a macroscopic system is independent of time on the average
and hence time is irrelevant in equilibrium. For the simple system considered in Problem 1.2 we
will see that counting the number of ways that the particles can be distributed between the two
halves of the box will give us much insight into the nature of equilibrium. This information tells
us nothing about the approach of the system to equilibrium, but it will give us insight into why
there is a direction of time.
Let us call each distinct arrangement of the particles between the two halves of the box a
configuration. A given particle can be in either the left half or the right half of the box. Because
the halves are equivalent, a given particle is equally likely to be in either half if the system is in
equilibrium. For N = 2, the four possible configurations are shown in Table 1.1. Note that each
configuration has a probability of 1/4 if the system is in equilibrium.
configuration n W(n)
L L 2 1
L R
R L 1 2
R R 0 1
Table 1.1: The four possible ways in which N = 2 particles can be distributed between the
two halves of a box. The quantity W(n) is the number of configurations corresponding to the
macroscopic state characterized by n.
Now let us consider N = 4 for which there are 2 × 2 × 2 × 2 = 24
= 16 configurations (see
Table 1.2). From a macroscopic point of view, we do not care which particle is in which half of the
box, but only the number of particles on the left. Hence, the macroscopic state or macrostate is
specified by n. Let us assume as before that all configurations are equally probable in equilibrium.
We see from Table 1.2 that there is only one configuration with all particles on the left and the
most probable macrostate is n = 2.

For larger N, the probability of the most probable macrostate with n = N/2 is much greater
than the macrostate with n = N, which has a probability of only 1/2N
corresponding to a single
configuration. The latter configuration is “special” and is said to be nonrandom, while the con-
figurations with n ≈ N/2, for which the distribution of the particles is approximately uniform,
are said to be “random.” So we can see that the equilibrium macrostate corresponds to the most
probable state.
configuration n W(n) P(n)
L L L L 4 1 1/16
R L L L 3
L R L L 3
L L R L 3
L L L R 3
4 4/16
R R L L 2
R L R L 2
R L L R 2
L R R L 2
L R L R 2
L L R R 2
6 6/16
R R R L 1
R R L R 1
R L R R 1
L R R R 1
4 4/16
R R R R 0 1 1/16
Table 1.2: The sixteen possible ways in which N = 4 particles can be distributed between the
two halves of a box. The quantity W(n) is the number of configurations corresponding to the
macroscopic state characterized by n. The probability P(n) of the macrostate n is calculated
assuming that each configuration is equally likely.
Problem 1.3. Enumeration of possible configurations
(a) Calculate the number of possible configurations for each macrostate n for N = 8 particles.
What is the probability that n = 8? What is the probability that n = 4? It is possible
to count the number of configurations for each n by hand if you have enough patience, but
because there are a total of 28
= 256 configurations, this counting would be very tedious. An
alternative is to derive an expression for the number of ways that n particles out of N can
be in the left half of the box. One way to motivate such an expression is to enumerate the
possible configurations for smaller values of N and see if you can observe a pattern.
(b) From part (a) we see that the macrostate with n = N/2 is much more probable than the
macrostate with n = N. Why?
We observed that if an isolated macroscopic system changes in time due to the removal of an
internal constraint, it tends to evolve from a less random to a more random state. We also observed

that once the system reaches its most random state, fluctuations corresponding to an appreciably
nonuniform state are very rare. These observations and our reasoning based on counting the
number of configurations corresponding to a particular macrostate allows us to conclude that
A system in a nonuniform macrostate will change in time on the average so as to
approach its most random macrostate where it is in equilibrium.
Note that our simulations involved watching the system evolve, but our discussion of the
number of configurations corresponding to each macrostate did not involve the dynamics in any
way. Instead this approach involved the enumeration of the configurations and assigning them
equal probabilities assuming that the system is isolated and in equilibrium. We will find that it is
much easier to understand equilibrium systems by ignoring the time altogether.
In the simulation of Problem 1.1 the total energy was conserved, and hence the macroscopic
quantity of interest that changed from the specially prepared initial state with n2 = N to the
most random macrostate with n2 ≈ N/3 was not the total energy. So what macroscopic quantity
changed besides n1, n2, and n3 (the number of particles in each third of the box)? Based on our
earlier discussion, we tentatively say that the quantity that changed is the entropy. This statement
is no more meaningful than saying that balls fall near the earth’s surface because of gravity. We
conjecture that the entropy is associated with the number of configurations associated with a
given macrostate. If we make this association, we see that the entropy is greater after the system
has reached equilibrium than in the system’s initial state. Moreover, if the system were initially
prepared in a random state, the mean value of n2 and hence the entropy would not change. Hence,
we can conclude the following:
The entropy of an isolated system increases or remains the same when an internal
constraint is removed.
This statement is equivalent to the second law of thermodynamics. You might want to skip to
Chapter 4, where this identification of the entropy is made explicit.
As a result of the two simulations that we have done and our discussions, we can make some
additional tentative observations about the behavior of macroscopic systems.
Fluctuations in equilibrium. Once a system reaches equilibrium, the macroscopic quantities of
interest do not become independent of the time, but exhibit fluctuations about their average values.
That is, in equilibrium only the average values of the macroscopic variables are independent of
time. For example, for the particles in the box problem n(t) changes with t, but its average value
n does not. If N is large, fluctuations corresponding to a very nonuniform distribution of the
particles almost never occur, and the relative fluctuations, σ/n become smaller as N is increased.
History independence. The properties of equilibrium systems are independent of their history.
For example, n would be the same whether we had started with n(t = 0) = 0 or n(t = 0) = N.
In contrast, as members of the human race, we are all products of our history. One consequence
of history independence is that it is easier to understand the properties of equilibrium systems by
ignoring the dynamics of the particles. (The global constraints on the dynamics are important.
For example, it is important to know if the total energy is a constant or not.) We will find that
equilibrium statistical mechanics is essentially equivalent to counting configurations. The problem
will be that this counting is difficult to do in general.

Need for statistical approach. Systems can be described in detail by specifying their microstate.
Such a description corresponds to giving all the information that is possible. For a system of
classical particles, a microstate corresponds to specifying the position and velocity of each particle.
In our analysis of Problem 1.2, we specified only in which half of the box a particle was located,
so we used the term configuration rather than microstate. However, the terms are frequently used
interchangeably.
From our simulations, we see that the microscopic state of the system changes in a complicated
way that is difficult to describe. However, from a macroscopic point of view, the description is
much simpler. Suppose that we simulated a system of many particles and saved the trajectories
of the particles as a function of time. What could we do with this information? If the number of
particles is 106
or more or if we ran long enough, we would have a problem storing the data. Do
we want to have a detailed description of the motion of each particle? Would this data give us
much insight into the macroscopic behavior of the system? As we have found, the trajectories of
the particles are not of much interest, and it is more useful to develop a probabilistic approach.
That is, the presence of a large number of particles motivates us to use statistical methods. In
Section 1.8 we will discuss another reason why a probabilistic approach is necessary.
We will find that the laws of thermodynamics depend on the fact that the number of particles in
macroscopic systems is enormous. A typical measure of this number is Avogadro’s number which
is approximately 6 × 1023
, the number of atoms in a mole. When there are so many particles,
predictions of the average properties of the system become meaningful, and deviations from the
average behavior become less and less important as the number of atoms is increased.
Equal a priori probabilities. In our analysis of the probability of each macrostate in Prob-
lem 1.2, we assumed that each configuration was equally probable. That is, each configuration of
an isolated system occurs with equal probability if the system is in equilibrium. We will make this
assumption explicit for isolated systems in Chapter 4.
Existence of different phases. So far our simulations of interacting systems have been restricted
to dilute gases. What do you think would happen if we made the density higher? Would a system
of interacting particles form a liquid or a solid if the temperature or the density were chosen
appropriately? The existence of different phases is investigated in Problem 1.4.
Problem 1.4. Different phases
(a) The applet/application at <stp.clarku.edu/simulations/lj.html> simulates an isolated
system of N particles interacting via the Lennard-Jones potential. Choose N = 64 and L = 18
so that the density ρ = N/L2
≈ 0.2. The initial positions are chosen at random except that
no two particles are allowed to be closer than σ. Run the simulation and satisfy yourself that
this choice of density and resultant total energy corresponds to a gas. What is your criterion?
(b) Slowly lower the total energy of the system. (The total energy is lowered by rescaling the
velocities of the particles.) If you are patient, you might be able to observe “liquid-like”
regions. How are they different than “gas-like” regions?
(c) If you decrease the total energy further, you will observe the system in a state roughly corre-
sponding to a solid. What is your criteria for a solid? Explain why the solid that we obtain in
this way will not be a perfect crystalline solid.

(d) Describe the motion of the individual particles in the gas, liquid, and solid phases.
(e) Conjecture why a system of particles interacting via the Lennard-Jones potential in (1.1) can
exist in different phases. Is it necessary for the potential to have an attractive part for the
system to have a liquid phase? Is the attractive part necessary for there to be a solid phase?
Describe a simulation that would help you answer this question.
It is fascinating that a system with the same interparticle interaction can be in different
phases. At the microscopic level, the dynamics of the particles is governed by the same equations
of motion. What changes? How does such a phase change occur at the microscopic level? Why
doesn’t a liquid crystallize immediately when its temperature is lowered quickly? What happens
when it does begin to crystallize? We will find in later chapters that phase changes are examples
of cooperative effects.
1.6 Measuring the pressure and temperature
The obvious macroscopic variables that we can measure in our simulations of the system of particles
interacting via the Lennard-Jones potential include the average kinetic and potential energies, the
number of particles, and the volume. We also learned that the entropy is a relevant macroscopic
variable, but we have not learned how to determine it from a simulation.4
We know from our
everyday experience that there are at least two other macroscopic variables that are relevant for
describing a macrostate, namely, the pressure and the temperature.
The pressure is easy to measure because we are familiar with force and pressure from courses
in mechanics. To remind you of the relation of the pressure to the momentum flux, consider N
particles in a cube of volume V and linear dimension L. The center of mass momentum of the
particles is zero. Imagine a planar surface of area A = L2
placed in the system and oriented
perpendicular to the x-axis as shown in Figure 1.3. The pressure P can be defined as the force per
unit area acting normal to the surface:
P =
Fx
A
. (1.2)
We have written P as a scalar because the pressure is the same in all directions on the average.
From Newton’s second law, we can rewrite (1.2) as
P =
1
A
d(mvx)
dt
. (1.3)
From (1.3) we see that the pressure is the amount of momentum that crosses a unit area of
the surface per unit time. We could use (1.3) to determine the pressure, but this relation uses
information only from the fraction of particles that are crossing an arbitrary surface at a given
time. Instead, our simulations will use the relation of the pressure to the virial, a quantity that
involves all the particles in the system.5
4We will find that it is very difficult to determine the entropy directly by making either measurements in the
laboratory or during a simulation. Entropy, unlike pressure and temperature, has no mechanical analog.
5See Gould, Tobochnik, and Christian, Chapter 8. The relation of the pressure to the virial is usually considered
in graduate courses in mechanics.

not done
Figure 1.3: Imaginary plane perpendicular to the x-axis across which the momentum flux is eval-
uated.
Problem 1.5. Nature of temperature
(a) Summarize what you know about temperature. What reasons do you have for thinking that
it has something to do with energy?
(b) Discuss what happens to the temperature of a hot cup of coffee. What happens, if anything,
to the temperature of its surroundings?
The relation between temperature and energy is not simple. For example, one way to increase
the energy of a glass of water would be to lift it. However, this action would not affect the
temperature of the water. So the temperature has nothing to do with the motion of the center of
mass of the system. As another example, if we placed a container of water on a moving conveyor
belt, the temperature of the water would not change. We also know that temperature is a property
associated with many particles. It would be absurd to refer to the temperature of a single molecule.
This discussion suggests that temperature has something to do with energy, but it has missed
the most fundamental property of temperature, that is, the temperature is the quantity that becomes
equal when two systems are allowed to exchange energy with one another. (Think about what
happens to a cup of hot coffee.) In Problem 1.6 we identify the temperature from this point of
view for a system of particles.
Problem 1.6. Identification of the temperature
(a) Consider two systems of particles interacting via the Lennard-Jones potential given in (1.1). Se-
lect the applet/application at <stp.clarku.edu/simulations/thermalcontact.html>. For
system A, we take NA = 81, AA = 1.0, and σAA = 1.0; for system B, we have NB = 64,
AA = 1.5, and σAA = 1.2. Both systems are in a square box with linear dimension L = 12. In
this case, toroidal boundary conditions are not used and the particles also interact with fixed
particles (with infinite mass) that make up the walls and the partition between them. Initially,
the two systems are isolated from each other and from their surroundings. Run the simulation
until each system appears to be in equilibrium. Does the kinetic energy and potential energy
of each system change as the system evolves? Why? What is the mean potential and kinetic
energy of each system? Is the total energy of each system fixed (to within numerical error)?

(b) Remove the barrier and let the two systems interact with one another.6
We choose AB = 1.25
and σAB = 1.1. What quantity is exchanged between the two systems? (The volume of each
system is fixed.)
(c) Monitor the kinetic and potential energy of each system. After equilibrium has been established
between the two systems, compare the average kinetic and potential energies to their values
before the two systems came into contact.
(d) We are looking for a quantity that is the same in both systems after equilibrium has been
established. Are the average kinetic and potential energies the same? If not, think about what
would happen if you doubled the N and the area of each system? Would the temperature
change? Does it make more sense to compare the average kinetic and potential energies or the
average kinetic and potential energies per particle? What quantity does become the same once
the two systems are in equilibrium? Do any other quantities become approximately equal?
What do you conclude about the possible identification of the temperature?
From the simulations in Problem 1.6, you are likely to conclude that the temperature is
proportional to the average kinetic energy per particle. We will learn in Chapter 4 that the
proportionality of the temperature to the average kinetic energy per particle holds only for a
system of particles whose kinetic energy is proportional to the square of the momentum (velocity).
Another way of thinking about temperature is that temperature is what you measure with a
thermometer. If you want to measure the temperature of a cup of coffee, you put a thermometer
into the coffee. Why does this procedure work?
Problem 1.7. Thermometers
Describe some of the simple thermometers with which you are familiar. On what physical principles
do these thermometers operate? What requirements must a thermometer have?
Now lets imagine a simulation of a simple thermometer. Imagine a special particle, a “demon,”
that is able to exchange energy with a system of particles. The only constraint is that the energy
of the demon Ed must be non-negative. The behavior of the demon is given by the following
algorithm:
1. Choose a particle in the system at random and make a trial change in one of its coordinates.
2. Compute ∆E, the change in the energy of the system due to the change.
3. If ∆E ≤ 0, the system gives the surplus energy |∆E| to the demon, Ed → Ed + |∆E|, and
the trial configuration is accepted.
4. If ∆E > 0 and the demon has sufficient energy for this change, then the demon gives the
necessary energy to the system, Ed → Ed − ∆E, and the trial configuration is accepted.
Otherwise, the trial configuration is rejected and the configuration is not changed.
6In order to ensure that we can continue to identify which particle belongs to system A and system B, we have
added a spring to each particle so that it cannot wander too far from its original lattice site.

Note that the total energy of the system and the demon is fixed.
We consider the consequences of these simple rules in Problem 1.8. The nature of the demon
is discussed further in Section 4.9.
Problem 1.8. The demon and the ideal gas
(a) The applet/application at <stp.clarku.edu/simulations/demon.html> simulates a demon
that exchanges energy with an ideal gas of N particles moving in d spatial dimensions. Because
the particles do not interact, the only coordinate of interest is the velocity of the particles.
In this case the demon chooses a particle at random and changes its velocity in one of its d
directions by an amount chosen at random between −∆ and +∆. For simplicity, the initial
velocity of each particle is set equal to +v0 ˆx, where v0 = (2E0/m)1/2
/N, E0 is the desired
total energy of the system, and m is the mass of the particles. For simplicity, we will choose
units such that m = 1. Choose d = 1, N = 40, and E0 = 10 and determine the mean energy
of the demon Ed and the mean energy of the system E. Why is E = E0?
(b) What is e, the mean energy per particle of the system? How do e and Ed compare for various
values of E0? What is the relation, if any, between the mean energy of the demon and the
mean energy of the system?
(c) Choose N = 80 and E0 = 20 and compare e and Ed. What conclusion, if any, can you make?7
(d) Run the simulation for several other values of the initial total energy E0 and determine how e
depends on Ed for fixed N.
(e) From your results in part (d), what can you conclude about the role of the demon as a
thermometer? What properties, if any, does it have in common with real thermometers?
(f) Repeat the simulation for d = 2. What relation do you find between e and Ed for fixed N?
(g) Suppose that the energy momentum relation of the particles is not = p2
/2m, but is = cp,
where c is a constant (which we take to be unity). Determine how e depends on Ed for fixed
N and d = 1. Is the dependence the same as in part (d)?
(h) Suppose that the energy momentum relation of the particles is = Ap3/2
, where A is a constant
(which we take to be unity). Determine how e depends on Ed for fixed N and d = 1. Is this
dependence the same as in part (d) or part (g)?
(i) The simulation also computes the probability P(Ed)δE that the demon has energy between
Ed and Ed +δE. What is the nature of the dependence of P(Ed) on Ed? Does this dependence
depend on the nature of the system with which the demon interacts?
7There are finite size effects that are order 1/N.

1.7 Work, heating, and the first law of thermodynamics
If you watch the motion of the individual particles in a molecular dynamics simulation, you would
probably describe the motion as “random” in the sense of how we use random in everyday speech.
The motion of the individual molecules in a glass of water would exhibit similar motion. Suppose
that we were to expose the water to a low flame. In a simulation this process would roughly
correspond to increasing the speed of the particles when they hit the wall. We say that we have
transferred energy to the system incoherently because each particle would continue to move more
or less at random.
You learned in your classical mechanics courses that the change in energy of a particle equals
the work done on it and the same is true for a collection of particles as long as we do not change
the energy of the particles in some other way at the same time. Hence, if we squeeze a plastic
container of water, we would do work on the system, and we would see the particles near the wall
move coherently. So we can distinguish two different ways of transferring energy to the system.
That is, heating transfers energy incoherently and doing work transfers energy coherently.
Lets consider a molecular dynamics simulation again and suppose that we have increased the
energy of the system by either compressing the system and doing work on it or by increasing the
speed of the particles that reach the walls of the container. Roughly speaking, the first way would
initially increase the potential energy of interaction and the second way would initially increase
the kinetic energy of the particles. If we increase the total energy by the same amount, could we
tell by looking at the particle trajectories after equilibrium has been reestablished how the energy
had been increased? The answer is no, because for a given total energy, volume, and number of
particles, the kinetic energy and the potential energy would have unique equilibrium values. (See
Problem 1.6 for a demonstration of this property.) We conclude that the energy of the gas can
be changed by doing work on it or by heating it. This statement is equivalent to the first law of
thermodynamics and from the microscopic point of view is simply a statement of conservation of
energy.
Our discussion implies that the phrase “adding heat” to a system makes no sense, because
we cannot distinguish “heat energy” from potential energy and kinetic energy. Nevertheless, we
frequently use the word “heat ” in everyday speech. For example, we might way “Please turn on
the heat” and “I need to heat my coffee.” We will avoid such uses, and whenever possible avoid
the use of the noun “heat.” Why do we care? Because there is no such thing as heat! Once upon
a time, scientists thought that there was a fluid in all substances called caloric or heat that could
flow from one substance to another. This idea was abandoned many years ago, but is still used in
common language. Go ahead and use heat outside the classroom, but we won’t use it here.
1.8 *The fundamental need for a statistical approach
In Section 1.5 we discussed the need for a statistical approach when treating macroscopic systems
from a microscopic point of view. Although we can compute the trajectory (the position and
velocity) of each particle for as long as we have patience, our disinterest in the trajectory of any
particular particle and the overwhelming amount of information that is generated in a simulation
motivates us to develop a statistical approach.

(a) (b)
Figure 1.4: (a) A special initial condition for N = 11 particles such that their motion remains
parallel indefinitely. (b) The positions of the particles at time t = 8.0 after the change in vx(6).
The only change in the initial condition from part (a) is that vx(6) was changed from 1 to 1.000001.
We now discuss why there is a more fundamental reason why we must use probabilistic meth-
ods to describe systems with more than a few particles. The reason is that under a wide variety of
conditions, even the most powerful supercomputer yields positions and velocities that are mean-
ingless! In the following, we will find that the trajectories in a system of many particles depend
sensitively on the initial conditions. Such a system is said to be chaotic. This behavior forces us
to take a statistical approach even for systems with as few as three particles.
As an example, consider a system of N = 11 particles moving in a box of linear dimension
L (see the applet/application at <stp.clarku.edu/simulations/sensitive.html>). The initial
conditions are such that all particles have the same velocity vx(i) = 1, vy(i) = 0, and the particles
are equally spaced vertically, with x(i) = L/2 for i = 1, . . . , 11 (see Fig. 1.4(a)). Convince yourself
that for these special initial conditions, the particles will continue moving indefinitely in the x-
direction (using toroidal boundary conditions).
Now let us stop the simulation and change the velocity of particle 6, such that vx(6) =
1.000001. What do you think happens now? In Fig. 1.4(b) we show the positions of the particles
at time t = 8.0 after the change in velocity of particle 6. Note that the positions of the particles
are no longer equally spaced and the velocities of the particles are very different. So in this case,
a small change in the velocity of one particle leads to a big change in the trajectories of all the
particles.
Problem 1.9. Irreversibility
The applet/application at <stp.clarku.edu/simulations/sensitive.html> simulates a system
of N = 11 particles with the special initial condition described in the text. Confirm the results that
we have discussed. Change the velocity of particle 6 and stop the simulation at time t and reverse

all the velocities. Confirm that if t is sufficiently short, the particles will return approximately to
their initial state. What is the maximum value of t that will allow the system to return to its
initial positions if t is replaced by −t (all velocities reversed)?
An important property of chaotic systems is their extreme sensitivity to initial conditions,
that is, the trajectories of two identical systems starting with slightly different initial conditions
will diverge exponentially in a short time. For such systems we cannot predict the positions
and velocities of the particles because even the slightest error in our measurement of the initial
conditions would make our prediction entirely wrong if the elapsed time is sufficiently long. That
is, we cannot answer the question, “Where is particle 2 at time t?” if t is sufficiently long. It might
be disturbing to realize that our answers are meaningless if we ask the wrong questions.
Although Newton’s equations of motion are time reversible, this reversibility cannot be realized
in practice for chaotic systems. Suppose that a chaotic system evolves for a time t and all the
velocities are reversed. If the system is allowed to evolve for an additional time t, the system will
not return to its original state unless the velocities are specified with infinite precision. This lack
of practical reversibility is related to what we observe in macroscopic systems. If you pour milk
into a cup of coffee, the milk becomes uniformly distributed throughout the cup. You will never
see a cup of coffee spontaneously return to the state where all the milk is at the surface because
to do so, the positions and velocities of the milk and coffee molecules must be chosen so that the
molecules of milk return to this very special state. Even the slightest error in the choice of positions
and velocities will ruin any chance of the milk returning to the surface. This sensitivity to initial
conditions provides the foundation for the arrow of time.
1.9 *Time and ensemble averages
We have seen that although the computed trajectories are meaningless for chaotic systems, averages
over the trajectories are physically meaningful. That is, although a computed trajectory might
not be the one that we thought we were computing, the positions and velocities that we compute
are consistent with the constraints we have imposed, in this case, the total energy E, the volume
V , and the number of particles N. This reasoning suggests that macroscopic properties such as
the temperature and pressure must be expressed as averages over the trajectories.
Solving Newton’s equations numerically as we have done in our simulations yields a time
average. If we do a laboratory experiment to measure the temperature and pressure, our mea-
surements also would be equivalent to a time average. As we have mentioned, time is irrelevant in
equilibrium. We will find that it is easier to do calculations in statistical mechanics by doing an
ensemble average. We will discuss ensemble averages in Chapter 3. In brief an ensemble average is
over many mental copies of the system that satisfy the same known conditions. A simple example
might clarify the nature of these two types of averages. Suppose that we want to determine the
probability that the toss of a coin results in “heads.” We can do a time average by taking one
coin, tossing it in the air many times, and counting the fraction of heads. In contrast, an ensemble
average can be found by obtaining many similar coins and tossing them into the air at one time.
It is reasonable to assume that the two ways of averaging are equivalent. This equivalence
is called the quasi-ergodic hypothesis. The use of the term “hypothesis” might suggest that the
equivalence is not well accepted, but it reminds us that the equivalence has been shown to be

rigorously true in only a few cases. The sensitivity of the trajectories of chaotic systems to initial
conditions suggests that a classical system of particles moving according to Newton’s equations of
motion passes through many different microstates corresponding to different sets of positions and
velocities. This property is called mixing, and it is essential for the validity of the quasi-ergodic
hypothesis.
In summary, macroscopic properties are averages over the microscopic variables and give
predictable values if the system is sufficiently large. One goal of statistical mechanics is to give
a microscopic basis for the laws of thermodynamics. In this context it is remarkable that these
laws depend on the fact that gases, liquids, and solids are chaotic systems. Another important
goal of statistical mechanics is to calculate the macroscopic properties from a knowledge of the
intermolecular interactions.
1.10 *Models of matter
There are many models of interest in statistical mechanics, corresponding to the wide range of
macroscopic systems found in nature and made in the laboratory. So far we have discussed a
simple model of a classical gas and used the same model to simulate a classical liquid and a solid.
One key to understanding nature is to develop models that are simple enough to analyze, but
that are rich enough to show the same features that are observed in nature. Some of the more
common models that we will consider include the following.
1.10.1 The ideal gas
The simplest models of macroscopic systems are those for which the interaction between the indi-
vidual particles is very small. For example, if a system of particles is very dilute, collisions between
the particles will be rare and can be neglected under most circumstances. In the limit that the
interactions between the particles can be neglected completely, the model is known as the ideal
gas. The classical ideal gas allows us to understand much about the behavior of dilute gases, such
as those in the earth’s atmosphere. The quantum version will be useful in understanding black-
body radiation (Section 6.9), electrons in metals (Section 6.10), the low temperature behavior of
crystalline solids (Section 6.12), and a simple model of superfluidity (Section 6.11).
The term “ideal gas” is a misnomer because it can be used to understand the properties of
solids and other interacting particle systems under certain circumstances, and because in many
ways the neglect of interactions is not ideal. The historical reason for the use of this term is that
the neglect of interparticle interactions allows us to do some calculations analytically. However,
the neglect of interparticle interactions raises other issues. For example, how does an ideal gas
reach equilibrium if there are no collisions between the particles?

1.10.2 Interparticle potentials
As we have mentioned, the most popular form of the potential between two neutral atoms is the
Lennard-Jones potential8
given in (1.1). This potential has an weak attractive tail at large r,
reaches a minimum at r = 21/6
σ ≈ 1.122σ, and is strongly repulsive at shorter distances. The
Lennard-Jones potential is appropriate for closed-shell systems, that is, rare gases such as Ar or Kr.
Nevertheless, the Lennard-Jones potential is a very important model system and is the standard
potential for studies where the focus is on fundamental issues, rather than on the properties of a
specific material.
An even simpler interaction is the hard core interaction given by
V (r) =
∞ (r ≤ σ)
0. (r > σ)
(1.4)
A system of particles interacting via (1.4) is called a system of hard spheres, hard disks, or hard
rods depending on whether the spatial dimension is three, two, or one, respectively. Note that
V (r) in (1.4) is purely repulsive.
1.10.3 Lattice models
In another class of models, the positions of the particles are restricted to a lattice or grid and the
momenta of the particles are irrelevant. In the most popular model of this type the “particles”
correspond to magnetic moments. At high temperatures the magnetic moments are affected by
external magnetic fields, but the interaction between moments can be neglected.
The simplest, nontrivial model that includes interactions is the Ising model, the most impor-
tant model in statistical mechanics. The model consists of spins located on a lattice such that
each spin can take on one of two values designated as up and down or ±1. The interaction energy
between two neighboring spins is −J if the two spins are in the same state and +J if they are
in opposite states. One reason for the importance of this model is that it is one of the simplest
to have a phase transition, in this case, a phase transition between a ferromagnetic state and a
paramagnetic state.
We will focus on three classes of models – the ideal classical and quantum gas, classical systems
of interacting particles, and the Ising model and its extensions. These models will be used in many
contexts to illustrate the ideas and techniques of statistical mechanics.
1.11 Importance of simulations
Only simple models such as the ideal gas or special cases such as the two-dimensional Ising model
can be analyzed by analytical methods. Much of what is done in statistical mechanics is to establish
the general behavior of a model and then relate it to the behavior of another model. This way of
understanding is not as strange as it first might appear. How many different systems in classical
mechanics can be solved exactly?
8This potential is named after John Lennard-Jones, 1894–1954, a theoretical chemist and physicist at Cambridge
University.

Statistical physics has grown in importance over the past several decades because powerful
computers and new computer algorithms have allowed us to explore the consequences of more com-
plex systems. Simulations play an important intermediate role between theory and experiment. As
our models become more realistic, it is likely that they will require the computer for understanding
many of their properties. In a simulation we start with a microscopic model for which the variables
represent the microscopic constituents and determine the consequences of their interactions. Fre-
quently the goal of our simulations is to explore these consequences so that we have a better idea
of what type of theoretical analysis might be possible and what type of laboratory experiments
should be done. Simulations allow us to compute many different kinds of quantities, some of which
cannot be measured in a laboratory experiment.
Not only can we simulate reasonably realistic models, we also can study models that are im-
possible to realize in the laboratory, but are useful for providing a deeper theoretical understanding
of real systems. For example, a comparison of the behavior of a model in three and four spatial
dimensions can yield insight into why the three-dimensional system behaves the way it does.
Simulations cannot replace laboratory experiments and are limited by the finite size of the
systems and by the short duration of our runs. For example, at present the longest simulations of
simple liquids are for no more than 10−6
s.
Not only have simulations made possible new ways of doing research, they also make it possible
to illustrate the important ideas of statistical mechanics. We hope that the simulations that we
have already discussed have already convinced you of their utility. For this reason, we will consider
many simulations throughout these notes.
1.12 Summary
This introductory chapter has been designed to whet your appetite, and at this point it is not likely
that you will fully appreciate the significance of such concepts as entropy and the direction of time.
We are reminded of the book, All I Really Need to Know I Learned in Kindergarten.9
In principle,
we have discussed most of the important ideas in thermodynamics and statistical physics, but it
will take you a while before you understand these ideas in any depth.
We also have not discussed the tools necessary to solve any problems. Your understanding of
these concepts and the methods of statistical and thermal physics will increase as you work with
these ideas in different contexts. You will find that the unifying aspects of thermodynamics and
statistical mechanics are concepts such as the nature of equilibrium, the direction of time, and
the existence of cooperative effects and different phases. However, there is no unifying equation
such as Newton’s second law of motion in mechanics, Maxwell’s equations in electrodynamics, and
Schrodinger’s equation in nonrelativistic quantum mechanics.
There are many subtleties that we have glossed over so that we could get started. For example,
how good is our assumption that the microstates of an isolated system are equally probable? This
question is a deep one and has not been completely answered. The answer likely involves the
nature of chaos. Chaos seems necessary to insure that the system will explore a large number of
the available microstates, and hence make our assumption of equal probabilities valid. However,
we do not know how to tell a priori whether a system will behave chaotically or not.
9Robert Fulghum, All I Really Need to Know I Learned in Kindergarten, Ballantine Books (2004).

Most of our discussion concerns equilibrium behavior. The “dynamics” in thermodynamics
refers to the fact that we can treat a variety of thermal processes in which a system moves from
one equilibrium state to another. Even if the actual process involves non-equilibrium states, we
can replace the non-equilibrium states by a series of equilibrium states which begin and end at
the same equilibrium states. This type of reasoning is analogous to the use of energy arguments
in mechanics. A ball can roll from the top of a hill to the bottom, rolling over many bumps and
valleys, but as long as there is no dissipation due to friction, we can determine the ball’s motion
at the bottom without knowing anything about how the ball got there.
The techniques and ideas of statistical mechanics are now being used outside of traditional
condensed matter physics. The field theories of high energy physics, especially lattice gauge theo-
ries, use the methods of statistical mechanics. New methods of doing quantum mechanics convert
calculations to path integrals that are computed numerically using methods of statistical mechan-
ics. Theories of the early universe use ideas from thermal physics. For example, we speak about
the universe being quenched to a certain state in analogy to materials being quenched from high
to low temperatures. We already have seen that chaos provides an underpinning for the need for
probability in statistical mechanics. Conversely, many of the techniques used in describing the
properties of dynamical systems have been borrowed from the theory of phase transitions, one of
the important areas of statistical mechanics.
Thermodynamics and statistical mechanics have traditionally been applied to gases, liquids,
and solids. This application has been very fruitful and is one reason why condensed matter physics,
materials science, and chemical physics are rapidly evolving and growing areas. Examples of new
materials include high temperature superconductors, low-dimensional magnetic and conducting
materials, composite materials, and materials doped with various impurities. In addition, scientists
are taking a new look at more traditional condensed systems such as water and other liquids,
liquid crystals, polymers, crystals, alloys, granular matter, and porous media such as rocks. And
in addition to our interest in macroscopic systems, there is growing interest is mesoscopic systems,
systems that are neither microscopic nor macroscopic, but are in between, that is, between ∼ 102
to ∼ 106
particles.
Thermodynamics might not seem to be as interesting to you when you first encounter it.
However, an understanding of thermodynamics is important in many contexts including societal
issues such as global warming, electrical energy production, fuel cells, and other alternative energy
sources.
The science of information theory uses many ideas from statistical mechanics, and recently, new
optimization methods such as simulated annealing have been borrowed from statistical mechanics.
In recent years statistical mechanics has evolved into the more general field of statistical
physics. Examples of systems of interest in the latter area include earthquake faults, granular mat-
ter, neural networks, models of computing, genetic algorithms, and the analysis of the distribution
of time to respond to email. Statistical physics is characterized more by its techniques than by the
problems that are its interest. This universal applicability makes the techniques more difficult to
understand, but also makes the journey more exciting.

Vocabulary
thermodynamics, statistical mechanics
macroscopic system
configuration, microstate, macrostate
specially prepared state, equilibrium, fluctuations
thermal contact, temperature
sensitivity to initial conditions
models, computer simulations
Problems
Problems page
1.1 7
1.2 9
1.3 11
1.4 13
1.5 and 1.6 15
1.7 16
1.8 17
1.9 19
Table 1.3: Listing of inline problems.
Problem 1.10. (a) What do you observe when a small amount of black dye is placed in a glass
of water? (b) Suppose that a video were taken of this process and the video was run backward
without your knowledge. Would you be able to observe whether the video was being run forward or
backward? (c) Suppose that you could watch a video of the motion of an individual ink molecule.
Would you be able to know that the video was being shown forward or backward?
Problem 1.11. Describe several examples based on your everyday experience that illustrate the
unidirectional temporal behavior of macroscopic systems. For example, what happens to ice placed
in a glass of water at room temperature? What happens if you make a small hole in an inflated
tire? What happens if you roll a ball on a hard surface?
Problem 1.12. In what contexts can we treat water as a fluid? In what context can water not
be treated as a fluid?
Problem 1.13. How do you know that two objects are at the same temperature? How do you
know that two bodies are at different temperatures?
Problem 1.14. Summarize your understanding of the properties of macroscopic systems.
Problem 1.15. Ask some of your friends why a ball falls when released above the Earth’s surface.
Explain why the answer “gravity” is not really an explanation.
Problem 1.16. What is your understanding of the concept of “randomness” at this time? Does
“random motion” imply that the motion occurs according to unknown rules?

Problem 1.17. What evidence can you cite from your everyday experience that the molecules in
a glass of water or in the surrounding air are in seemingly endless random motion?
Problem 1.18. Write a brief paragraph on the meaning of the abstract concepts, “energy” and
“justice.” (See the Feynman Lectures, Vol. 1, Chapter 4, for a discussion of why it is difficult to
define such abstract concepts.)
Problem 1.19. A box of glass beads is also an example of macroscopic systems if the number
of beads is sufficiently large. In what ways such a system different than the macroscopic systems
that we have discussed in this chapter?
Problem 1.20. Suppose that the handle of a plastic bicycle pump is rapidly pushed inward.
Predict what happens to the temperature of the air inside the pump and explain your reasoning.
(This problem is given here to determine how you think about this type of problem at this time.
Similar problems will appear in later chapters to see if and how your reasoning has changed.)
Appendix 1A: Mathematics Refresher
As discussed in Sec. 1.12, there is no unifying equation in statistical mechanics such as Newton’s
second law of motion to be solved in a variety of contexts. For this reason we will not adopt one
mathematical tool. Appendix 2B summarizes the mathematics of thermodynamics which makes
much use of partial derivatives. Appendix A summarizes some of the mathematical formulas and
relations that we will use. If you can do the following problems, you have a good background for
most of the mathematics that we will use in the following chapters.
Problem 1.21. Calculate the derivative with respect to x of the following functions: ex
, e3x
, eax
,
ln x, ln x2
, ln 3x, ln 1/x, sin x, cos x, sin 3x, and cos 2x.
Problem 1.22. Calculate the following integrals:
2
1
dx
2x2
(1.5a)
2
1
dx
4x
(1.5b)
2
1
e3x
dx (1.5c)
Problem 1.23. Calculate the partial derivative of x2
+ xy + 3y2
with respect to x and y.
Suggestions for Further Reading
P. W. Atkins, The Second Law, Scientific American Books (1984). A qualitative introduction to
the second law of thermodynamics and its implications.

J. G. Oliveira and A.-L. Barab´asi, “Darwin and Einstein correspondence patterns,” Nature 437,
1251 (2005). The authors found the probability that Darwin and Einstein would respond to
a letter in τ days is well approximated by a power law, P(τ) ∼ τ−a
with a ≈ 3/2. What
is the explanation for this power law behavior? How long does it take you to respond to an
email?
Manfred Eigen and Ruthild Winkler, How the Principles of Nature Govern Chance, Princeton
University Press (1993).
Richard Feynman, R. B. Leighton, and M. Sands, Feynman Lectures on Physics, Addison-Wesley
(1964). Volume 1 has a very good discussion of the nature of energy and work.
Harvey Gould, Jan Tobochnik, and Wolfgang Christian, An Introduction to Computer Simulation
Methods, third edition, Addison-Wesley (2006).
F. Reif, Statistical Physics, Volume 5 of the Berkeley Physics Series, McGraw-Hill (1967). This
text was the ﬁrst to make use of computer simulations to explain some of the basic properties
of macroscopic systems.
Jeremy Rifkin, Entropy: A New World View, Bantom Books (1980). Although this popular book
raises some important issues, it, like many other popular books articles, misuses the concept
of entropy. For more discussion on the meaning of entropy and how it should be introduced,
see <www.entropysite.com/> and <www.entropysimple.com/>.

Chapter 2
Thermodynamic Concepts and
Processes
c 2005 by Harvey Gould and Jan Tobochnik
29 September 2005
The study of temperature, energy, work, heating, entropy, and related macroscopic concepts com-
prise the field known as thermodynamics.
2.1 Introduction
In this chapter we will discuss ways of thinking about macroscopic systems and introduce the basic
concepts of thermodynamics. Because these ways of thinking are very different from the ways that
we think about microscopic systems, most students of thermodynamics initially find it difficult
to apply the abstract principles of thermodynamics to concrete problems. However, the study of
thermodynamics has many rewards as was appreciated by Einstein:
A theory is the more impressive the greater the simplicity of its premises, the more
different kinds of things it relates, and the more extended its area of applicability.
Therefore the deep impression that classical thermodynamics made to me. It is the only
physical theory of universal content which I am convinced will never be overthrown,
within the framework of applicability of its basic concepts.1
The essence of thermodynamics can be summarized by two laws: (1) Energy is conserved
and (2) entropy increases. These statements of the laws are deceptively simple. What is energy?
You are probably familiar with the concept of energy from other courses, but can you define it?
Abstract concepts such as energy and entropy are not easily defined nor understood. However, as
you apply these concepts in a variety of contexts, you will gradually come to understand them.
1A. Einstein, Autobiographical Notes, Open Court Publishing Company (1991).
26

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